【正文】 all architecture of our system is illustrated in Figure 2. To control the mobile platform, in this case a Segway, two low level behaviors are use: One for target acquisition and one for obstacle avoidance. Using petitive dynamics these are fused together to provide the Mobile behavior, which specifies the desired motion of the mobile platform. Similarly we have target acquisition and obstacle avoidance behaviors for the manipulator fused together based on petitive dynamics, to give the Manipulator Acquisition behavior. When the target is not within reach, the manipulator should retract to a safe configuration, which is the purpose of the 19 Manipulator Retract behavior. The last fusion bines the controls in a safe manner, such that the target acquisition and retract behaviors do not disturb one another and the mobile platform does not start moving towards a new target before the manipulator has been retracted. Fig. 2. Overall architecture of the control system Using weights wmobile ， manipacquisitionw and manipretractw to represent the influence of the Mobile, Manipulator Acquisition and Manipulator Retract behaviors, the control signals mobileu and manipq for the mobile platform and the manipulator are given by ? ?leftrightumobile mobile uuw? （ 1） m a n ipm a n ip m a n ipm a n ip m a n ipa c q u is itio na c q u is itio n r e tr a c t r e tr a c tqq qww?? (2) Where （ leftu rightu ） are control inputs to the left and right wheels of the platform as described in Section III, manipacquisitionq and manipretractq are the manipulator joint velocities as described in Section IV. A. Competitive Dynamics The petitive dynamics approach used is based on [12], but with the additional parameter bT used to control the transition rate as in [14]. The dynamical system used is thus given by 39。39。3 39。 2( ) ,b b b b b bbbbT w a w w r b b w w n o is e?? ? ? ?? （ 3） 20 In which ba is the petitive advantage of behavior b and r 39。b ,b is the petitive interaction of behavior 39。b upon b. 1) Mobile: The petitive advantages of the mobile platform should strengthen the behavior when far away from the target and reduce it when the target is reached. This is achieved through ta nh ( ( ) )m ob ile m ob ile m ob ilea tar thr e sh olda k d d?? （ 4） In which mobileak determines how rapidly the advantage should change, tard is the distance to the target and mobilethresholdd specifies a minimum distance to the target required before the mobile platform should move. The mobile behavior has no ability to interact and suppress other behaviors, thus its petitive interactions are set to 0. 2) Manipulator Acquisition: This behavior should be strengthened when the mobile platform gets close to its target. The petitive advantage will thus be defined as ta n h ( ( ) )m an ip m an ip m an ipac uis iti on a tar thr e s ho lda k d d? ? ? （ 5） The activation distance manipthresholdd must be greater than mobilethresholdd to make sure the behavior is activated. This behavior has no direct interaction with the others, thus its interactions are set to 0. 3) Manipulator Retract: The retract behavior should be activated opposite the goal behavior, hence manip manipretract acqisitionaa?? ta nh ( ( ) )m an ip m ob il ea tar thre sh oldk d d （ 6） Except for a very small transition time this prevents the manipulators acquisition and retract behaviors from being active at the same time, thus we can set , 0retract acquisitionr ?. For the interaction between the retract and the mobile behaviors we wish retract to deactivate mobile when the manipulator is far away from its home configuration. The interaction is therefore defined as hom, 1 ( 1 ta n h ( ( ) ) )2 r e t r a c tr c u r r e n t e qr e t r a c t a c q u i s i t i o n k q qr ?? ? ? ? （ 7） 21 In which curq and homeq are the manipulators current and home configurations, ∈ q specifies a proximity distance around homeq and retractrk specifies how quickly the interaction changes. III. CONTROL OF THE MOBILE PLATFORM The control of the mobile platform is constructed very similar to what is presented in [14], but with a few differences. First of all only the target acquisition and obstacle avoidance behaviors are used. The corridor following and wall avoidance are not included, but would be straight forward extensions. The second area in which this work differs is in how the density of obstacles is calculated. Details of this will be explained in section IIID. For the control to actually be able to navigate through the environment, it is necessary with a method for localization. The approach we have used is based on the method described in [20], which bines odometry and laser range measurements matched against a map of dominating lines in the environment. The control of the platform is encoded using the orientation, ? and the velocity, ? , which results in a system with control inputs ? ?,mobilefv?? 。 The values of mobilef are made up of two parts, mobiletarf and mobileobsf , which are bined as m o b ile m o b ile m o b ilem o b ile m o b ileta r o b sta r o b sf f fww?? （ 8） Where the weights mobiletarw and mobileobsw are controlled using Eq. (3) with the petitive advantage and interactions described in section IIIC. As control input we need expressions for the left and right wheels of the mobile platform, denoted leftu and rightu , respectively. To obtain these v is integrated to get v, which together with the desired rotational velocity? , the wheel diameter wheeld and the distance between the wheels wheelbased can be used to calculate the control inputs as ( , ) 2left w heelvv du ? ? ???